While dealing with query execution and analysis, optimization procedures are usually followed to predict performance of query with respect to a particular system or database. In order to clearly predict the performance of query in advance, growth or variation of database with time should be considered. The varying size of database may drastically affect the query performance time.
There are so many factors associated with the database that may affect the query response. Such factors may include access pattern of the query. Most of the existing technology methods predict the query response time on a system based on the past history of the executed queries on the database system or by using Machine Learning (ML) approach. Such approaches may differ in applying different ML techniques and also these approaches are not suitable at an application development stage. Also, use of past queries may increase error possibility.
Further, in some of the proposed solutions, changes in the Database (DB) server internals is required which may not be a feasible option. Although, some of the solutions have made use of database cost utility only to understand the query access plan on large size database. However, little thought has been given to dealing with increase in data volume while checking the query performance.